app.py

import gradio as gr

# Placeholder for model loading (adjust as needed for your specific models)
# In a real application, these would load actual models
def load_model(model_name):
    print(f"Loading {model_name}...")  # Indicate model loading
    # Simulate different model behaviors (replace with actual model logic)
    if model_name == "DeepSeek-R1-Distill-Qwen-32B":
        return lambda input_text, history: f"Distilled Model Response to: {input_text}"
    elif model_name == "DeepSeek-R1":
        return lambda input_text, history: f"Base Model Response to: {input_text}"
    elif model_name == "DeepSeek-R1-Zero":
        return lambda input_text, history: f"Zero Model Response to: {input_text}"
    else:
        return lambda input_text, history: f"Default Response to: {input_text}"

# Load the models (placeholder functions here)
deepseek_r1_distill = load_model("DeepSeek-R1-Distill-Qwen-32B")
deepseek_r1 = load_model("DeepSeek-R1")
deepseek_r1_zero = load_model("DeepSeek-R1-Zero")

# --- Chatbot function ---
def chatbot(input_text, history, model_choice, system_message, max_new_tokens, temperature, top_p):
    history = history or []
    print(f"Input: {input_text}, History: {history}, Model: {model_choice}")

    # Choose the model based on user selection
    if model_choice == "DeepSeek-R1-Distill-Qwen-32B":
        model_function = deepseek_r1_distill
    elif model_choice == "DeepSeek-R1":
        model_function = deepseek_r1
    elif model_choice == "DeepSeek-R1-Zero":
        model_function = deepseek_r1_zero
    else:
        model_function = lambda x, h: "Please select a model."

    # Simulate model response with parameters (replace with actual model inference)
    # In a real application, you would pass these parameters to your model
    response = model_function(input_text, history)
    response = f"**System Message:** {system_message}\n\n**Model Response:** {response}\n\n" \
               f"**Parameters Used:**\n- Max New Tokens: {max_new_tokens}\n- Temperature: {temperature}\n- Top-p: {top_p}"

    history.append((input_text, response))
    return history, history, ""  # Update both chatbot output and state


# --- Gradio Interface ---
with gr.Blocks(theme=gr.themes.Soft()) as demo:  # Apply a theme
    gr.Markdown(
        """
        # DeepSeek Chatbot
        Created by [ruslanmv.com](https://ruslanmv.com/)
        
        This is a demo of different DeepSeek models. Select a model, type your message, and click "Submit".
        You can also adjust optional parameters like system message, max new tokens, temperature, and top-p.
        """
    )

    with gr.Row():
        with gr.Column(scale=4): # Make chatbot take more space
            chatbot_output = gr.Chatbot(label="DeepSeek Chatbot", height=500)
            msg = gr.Textbox(label="Your Message", placeholder="Type your message here...")

            with gr.Row():
                submit_btn = gr.Button("Submit", variant="primary")
                clear_btn = gr.ClearButton([msg, chatbot_output])
        
        with gr.Column(scale=1):
            model_choice = gr.Radio(
                choices=["DeepSeek-R1-Distill-Qwen-32B", "DeepSeek-R1", "DeepSeek-R1-Zero"],
                label="Choose a Model",
                value="DeepSeek-R1"  # Default model
            )

            with gr.Accordion("Optional Parameters", open=False):
                system_message = gr.Textbox(
                    label="System Message",
                    value="You are a friendly Chatbot created by ruslanmv.com",
                    lines=2,
                )
                max_new_tokens = gr.Slider(
                    minimum=1, maximum=4000, value=200, label="Max New Tokens"
                )
                temperature = gr.Slider(
                    minimum=0.10, maximum=4.00, value=0.70, label="Temperature"
                )
                top_p = gr.Slider(
                    minimum=0.10, maximum=1.00, value=0.90, label="Top-p (nucleus sampling)"
                )

    # Maintain chat history
    chat_history = gr.State([])

    # Event handling
    submit_btn.click(
        chatbot,
        [msg, chat_history, model_choice, system_message, max_new_tokens, temperature, top_p],
        [chatbot_output, chat_history, msg],
    )
    msg.submit(
        chatbot,
        [msg, chat_history, model_choice, system_message, max_new_tokens, temperature, top_p],
        [chatbot_output, chat_history, msg],
    )

# Launch the demo
if __name__ == "__main__":
    demo.launch()